1 신경망 모형 1

1.1 뇌신경

1.2 신경망 모사

1.3 수식

\[ x \beta = \beta_0 + \beta_1X_i + \cdots + \beta_nX_n \\ \] \[ prob = {\frac{exp(x\beta)}{1 + exp (x\beta)}} \]

\[ prob = \frac{exp( \beta_0 + \beta_1X_i + \cdots + \beta_nX_n )} {1 + exp ( \beta_0 + \beta_1X_i + \cdots + \beta_nX_n)} \]

\[ prob = \frac {1} {1 + e^{ -( \beta_0 + \beta_1X_i + \cdots + \beta_nX_n) }} \]

2 이항 회귀모형

2.1 데이터

2.1.1 요약 통계량

Data summary
Name Piped data
Number of rows 20
Number of columns 2
_______________________
Column type frequency:
numeric 2
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
학습시간 0 1 2.79 1.51 0.5 1.69 2.62 4.06 5.5 ▇▇▆▅▅
입학여부 0 1 0.50 0.51 0.0 0.00 0.50 1.00 1.0 ▇▁▁▁▇

2.2 시각화

2.3 모형


Call:  glm(formula = 입학여부 ~ 학습시간, family = "binomial", 
    data = lr_tbl)

Coefficients:
(Intercept)     학습시간  
     -4.078        1.505  

Degrees of Freedom: 19 Total (i.e. Null);  18 Residual
Null Deviance:      27.73 
Residual Deviance: 16.06    AIC: 20.06

2.4 합격 예측

  1. Illustrating (Convolutional) Neural Networks in LaTeX with TikZ↩︎

 

데이터 과학자 이광춘 저작

kwangchun.lee.7@gmail.com